Best-Practices

Best Practices for AI-Augmented Development

Overview

Table of Contents

1. AI Tools Landscape
2. Communication Best Practices
3. Code Quality Practices
4. Workflow Integration Practices
5. Project Structure Best Practices
6. Team Size Adaptations
7. Testing and Quality Assurance
8. Common Pitfalls and Solutions
9. Success Metrics
10. Continuous Improvement

AI Tools Landscape

Rapid Prototyping Tools / Web-IDEs

• Lovable.dev, Lovable
• V0.dev v0
• Bolt.new Bolt.new
• Manus.im Manus.im

• Exploring new ideas without precise requirements
• Need quick validation of concepts
• Working with non-technical stakeholders

• Less effective for improving existing products
• Struggle with large codebases
• Limited for very specific behavioral requirements

Copilots/Coding Assistants

• Cursor
• Windsurf IDE
• VS Code with GitHub Copilot
• Cline
• Cody
• Kiro
• DX.ai

• Careful documentation of system features and coding guidelines
• Clear action plans for AI including step-by-step problem solving
• Comprehensive self-checking procedures

Coding Agents

Offline / CLI / In-Editor

• Claude Code
• Aider
• OpenAI Codex CLI
• Amazon Q Developer
• Smol AI Developer
• TabbyML

• Major refactoring and technology migrations
• Routine code changes across large codebases
• Framework migrations (can save weeks of development time)

• Frontend framework migration
• TypeScript to Go migration

Cloud-Based

• Devin.ai, Devin
• Sweep
• Replit, Replit

• Integration with cloud infrastructure
• Task assignment via Slack or GitHub issues
• Full team member interaction model
• Pull request generation and iteration

• Small routine tasks and bug fixes
• Package version updates
• Pull request reviews

• Code base quality and documentation
• Clear task specification and directions
• Underlying LLM model capabilities

Testing and Quality Assurance Tools

• Octomind
• Carbonate
• Meticulous

• Sauce Labs
• WebDriver.io
• Playwright Playwright
• BrowserStack
• LambdaTest

Workflow Automation

• N8N n8n
• Zapier Zapier
• Make Make

• Customer onboarding automation
• Cold outreach/drip campaigns
• Bug report triage and issue generation
• Support ticket auto-tagging
• Social proof/testimonial collection
• Content generation and publishing pipelines

An Aside on Model Context Protocol

• Ref Tools

Data Visibility Tools / RAG

• Microsoft Fabric
• Power BI Copilot

Communication Best Practices

Effective AI Prompting

✅ DO: Be Specific and Detailed

❌ "Create a data import feature"

✅ "Create a React TypeScript CSV import component for RecordCollector microfrontend:
- File upload with drag-and-drop support
- CSV validation with error highlighting
- Preview of first 5 rows before import
- Progress bar for large file processing
- Integration with Zustand store for data management
- Tailwind CSS styling matching shared design system
- Error handling for malformed CSV files
- Module federation compatibility for main shell integration"

## Task: Implement HighlightCollector Content Extraction

### Requirements:
- Text selection and highlighting with visual feedback
- Keyword extraction using AI-assisted analysis
- Export highlights to JSON, CSV, and PDF formats
- Real-time collaboration for team highlighting
- Integration with InsightAssembler for data synthesis

### Technical Constraints:
- React 18 with TypeScript 5+
- Module federation for microfrontend architecture
- Zustand for local state management
- Integration with existing event bus system
- Performance optimization for large documents (1000+ pages)

### Acceptance Criteria:
- [ ] Users can select and highlight text with visual feedback
- [ ] AI extracts relevant keywords and phrases automatically
- [ ] Highlights synchronize across team members in real-time
- [ ] Export functionality works for all specified formats
- [ ] Component loads seamlessly in main shell application
- [ ] Performance remains smooth with 500+ highlights per document

✅ DO: Provide Context and Constraints

• Share relevant existing code snippets
• Specify technology stack and versions
• Include design system requirements
• Mention performance constraints
• List integration requirements

✅ DO: Use Structured Formats

## Task: [Brief description]

### Requirements:
- Functional requirement 1
- Functional requirement 2

### Technical Constraints:
- Technology stack
- Performance requirements
- Security considerations

### Acceptance Criteria:
- [ ] Criterion 1
- [ ] Criterion 2

Iteration Patterns

Start Small, Build Up

1. Use Version Control: At every step use version control hygiene.
2. MVP First: Get basic functionality working
3. Validate Early: Test core assumptions
4. Iterate Quickly: Make small, frequent improvements
5. Add Complexity: Layer on advanced features
6. Assume rewrites: Assume that some massive code generation will need to be reset and rewritten.

Feedback Loops

• Review AI output before integration
• Test immediately after implementation
• Document what works and what doesn't
• Refine prompts based on results

Code Quality Practices

AI Code Review Checklist

Security Review

• No hardcoded secrets or API keys
• Proper input validation and sanitization
• Authentication and authorization checks
• SQL injection prevention
• XSS protection measures

Performance Review

• Efficient algorithms and data structures
• Proper caching strategies
• Database query optimization
• Memory usage considerations
• Network request efficiency

Maintainability Review

• Clear, descriptive naming conventions
• Proper code organization and structure
• Adequate error handling
• Comprehensive logging
• Documentation and comments where needed

Testing Strategies

AI-Generated Test Coverage

• Unit tests for core functionality
• Integration tests for API endpoints
• End-to-end tests for critical user flows
• Error condition and edge case testing

// AI-generated test for RecordCollector CSV import
describe('CSV Import Functionality', () => {
  it('should validate CSV format and show preview', async () => {
    const csvFile = new File(['name,email\nJohn,john@test.com'], 'test.csv');
    const { getByTestId } = render(<CSVImporter />);
    
    fireEvent.drop(getByTestId('csv-dropzone'), {
      dataTransfer: { files: [csvFile] }
    });
    
    await waitFor(() => {
      expect(getByTestId('csv-preview')).toBeInTheDocument();
      expect(getByTestId('import-button')).not.toBeDisabled();
    });
  });
});

// AI-generated integration test for microfrontend communication
describe('Cross-MFE Communication', () => {
  it('should propagate record selection across microfrontends', async () => {
    const { emit } = useEventBus();
    const mockRecord = { id: '123', name: 'Test Record' };
    
    emit('record:selected', { recordId: '123', record: mockRecord });
    
    // Verify other microfrontends receive the event
    await waitFor(() => {
      expect(mockPromptManagerHandler).toHaveBeenCalledWith({
        recordId: '123',
        record: mockRecord
      });
    });
  });
});

Human Validation Requirements

• Manual testing of complex user interactions
• Security penetration testing
• Performance benchmarking
• Accessibility testing with real users

• Complete data workflow: import → process → review → export
• Module federation loading across all microfrontends
• Cross-browser compatibility (Chrome, Firefox, Safari, Edge)
• Mobile responsiveness for tablet usage
• Large dataset performance (10,000+ records)
• Concurrent user collaboration testing
• AI service integration and fallback handling
• Security validation for file uploads and data processing

Workflow Integration Practices

Development Workflow

Phase 1: Planning and Specification

1. Define clear requirements with stakeholders
2. Create detailed technical specifications
3. Break down work into small, manageable tasks
4. Prepare context and reference materials for AI

Phase 2: AI-Assisted Implementation

1. Generate initial implementations with AI
2. Review and refactor AI-generated code
3. Write comprehensive tests
4. Validate against specifications

Phase 3: Human Quality Assurance

1. Code review by experienced developers
2. Integration testing with existing systems
3. User acceptance testing
4. Performance and security validation

Team Collaboration

Role Distribution

• AI: Boilerplate code, initial implementations, test generation
• Junior Developers: Code review, testing, documentation
• Senior Developers: Architecture decisions, complex problem solving
• Product Owners: Requirements validation, acceptance criteria

RecordCollector Development:
├── AI Generated (70%)
│   ├── CSV parsing logic and validation
│   ├── Data table component with sorting/filtering
│   ├── Unit tests for core functionality
│   └── Initial API endpoint implementations
│
├── Junior Developer Tasks (20%)
│   ├── Code review and quality assurance
│   ├── Manual testing of user workflows
│   ├── Documentation updates
│   └── Bug fixes and minor enhancements
│
├── Senior Developer Tasks (8%)
│   ├── Module federation architecture decisions
│   ├── Performance optimization strategies
│   ├── Security implementation and review
│   └── Complex integration problem solving
│
└── Product Owner Tasks (2%)
    ├── Feature specification and acceptance criteria
    ├── User story validation and prioritization
    ├── Stakeholder communication and alignment
    └── Release planning and roadmap management

Knowledge Sharing

• Document successful AI prompts and patterns
• Share effective workflow templates
• Conduct regular retrospectives on AI usage
• Create internal knowledge base of best practices

# Successful Prompt Library for Augment-It

## Microfrontend Component Generation
"Create a React TypeScript microfrontend component with module federation support..."

## State Management Setup
"Implement Zustand store for [MicrofrontendName] with the following state structure..."

## API Integration Pattern
"Create API service layer for [MicrofrontendName] with React Query integration..."

## Testing Pattern
"Generate comprehensive test suite for [ComponentName] including unit, integration, and E2E tests..."

Project Structure Best Practices

Documentation Standards

AI Interaction Logs

docs/ai-interactions/
├── prompts/
│   ├── microfrontend-generation.md
│   ├── module-federation-setup.md
│   ├── component-creation-patterns.md
│   ├── api-endpoint-creation.md
│   ├── state-management-setup.md
│   └── test-generation.md
├── successful-patterns/
│   ├── record-collector-implementation.md
│   ├── cross-mfe-communication.md
│   ├── data-import-workflows.md
│   ├── performance-optimization.md
│   └── error-handling-strategies.md
├── lessons-learned/
│   ├── week-1-foundation-setup.md
│   ├── week-4-microfrontend-integration.md
│   ├── week-8-performance-optimization.md
│   └── project-completion-retrospective.md
└── augment-it-specific/
    ├── data-augmentation-workflow.md
    ├── ai-service-integration.md
    ├── collaborative-highlighting.md
    └── insight-assembly-patterns.md

augment-it-project/
├── apps/
│   ├── main-shell/
│   │   ├── README.md                    # Host application overview
│   │   ├── ARCHITECTURE.md              # Module federation setup
│   │   └── docs/
│   │       ├── deployment.md
│   │       └── troubleshooting.md
│   │
│   ├── record-collector/
│   │   ├── README.md                    # MFE specific documentation
│   │   ├── API.md                       # Component API documentation
│   │   ├── TESTING.md                   # Testing strategy and examples
│   │   └── ai-prompts/                  # AI prompts used for this MFE
│   │       ├── component-generation.md
│   │       ├── test-creation.md
│   │       └── integration-setup.md
│   │
│   └── [other-microfrontends]/
│       └── [same structure as above]
│
├── docs/
│   ├── architecture/
│   │   ├── system-overview.md
│   │   ├── microfrontend-patterns.md
│   │   └── data-flow-diagrams.md
│   ├── ai-development/
│   │   ├── prompt-library.md
│   │   ├── successful-patterns.md
│   │   ├── code-review-guidelines.md
│   │   └── quality-assurance.md
│   └── user-guides/
│       ├── getting-started.md
│       ├── data-import-guide.md
│       └── workflow-tutorials.md
└── packages/
    ├── shared-ui/
    │   ├── README.md                    # Component library documentation
    │   ├── STORYBOOK.md                 # Storybook setup and usage
    │   └── components/
    │       └── [component-name]/
    │           ├── README.md            # Component-specific docs
    │           ├── examples.md          # Usage examples
    │           └── ai-generation.md     # AI prompts for this component
    │
    └── shared-utils/
        ├── README.md                    # Utility functions documentation
        ├── API.md                       # API documentation
        └── ai-patterns/
            ├── utility-generation.md
            └── testing-patterns.md

Living Specifications

• Keep specifications up-to-date with changes
• Version control all specification documents
• Link specifications to related code files
• Regular review and refinement sessions

Code Organization

AI-Friendly Patterns

• Consistent naming conventions across the project
• Clear separation of concerns
• Well-defined interfaces and contracts
• Comprehensive type definitions

Template Structures

src/
├── components/
│   ├── __templates__/
│   │   ├── component-template.tsx
│   │   └── component-spec.md
│   └── ...
├── services/
│   ├── __templates__/
│   │   ├── service-template.ts
│   │   └── service-spec.md
│   └── ...
└── utils/
    ├── __templates__/
    └── ...

Team Size Adaptations

Solo Product Owner (0 Developers)

• Leverage rapid prototyping tools for concept validation
• Focus on user experience validation through interactive demos
• Build demo-ready MVPs for stakeholder presentations
• Validate market assumptions before committing development resources
• Create clear handoff documentation for future development teams
• Use AI for requirements refinement and technical specification
• Maintain prototype portfolio for product evolution tracking

Small Teams (2-5 developers)

• Focus on rapid prototyping and iteration
• Use AI for maximum automation of boilerplate code
• Implement lightweight review processes
• Prioritize speed and flexibility
• Consider rapid prototyping tools for validation

Medium Teams (6-15 developers)

• Establish clear AI usage guidelines and standards
• Implement structured code review processes
• Create shared libraries of AI prompts and patterns
• Regular team training and knowledge sharing
• Adopt coding assistants for productivity gains

Large Teams (15+ developers)

• Formal AI integration policies and procedures
• Dedicated AI workflow specialists or champions
• Enterprise-grade security and compliance reviews
• Comprehensive metrics and performance tracking
• Consider cloud-based coding agents for routine tasks

Testing and Quality Assurance

AI-Enhanced Testing Strategy

• Leverage AI testing tools for automated E2E test generation
• Maintain traditional testing practices as foundation
• Use AI for test data generation and edge case identification
• Implement continuous quality monitoring

Quality Gates

• All AI-generated code must pass same quality standards
• Mandatory security reviews for AI-generated components
• Performance benchmarking for AI-optimized code
• Accessibility compliance verification

Common Pitfalls and Solutions

Pitfall: Over-Reliance on AI

Pitfall: Inconsistent Code Quality

Pitfall: Poor Prompt Engineering

Pitfall: Integration Issues

Pitfall: Tool Selection Mismatch

Success Metrics

Quantitative Metrics

• Development Velocity: Lines of code per developer per day
• Code Quality: Defect rates, code coverage, technical debt
• Team Productivity: Story points completed per sprint
• AI Effectiveness: Percentage of AI suggestions accepted
• Tool ROI: Time saved vs. tool costs

Qualitative Metrics

• Developer Satisfaction: Survey feedback on AI tool usage
• Code Maintainability: Ease of making changes to AI-generated code
• Learning Curve: Time for new team members to become productive
• Problem-Solving Capability: Complex issues resolved with AI assistance

Continuous Improvement

Regular Review Processes

• Weekly AI usage retrospectives
• Monthly pattern and template updates
• Quarterly workflow optimization reviews
• Annual strategy and tool evaluation

Knowledge Management

• Maintain searchable database of successful patterns
• Regular updates to best practice documentation
• Cross-team sharing of effective techniques
• External community engagement and learning

Tool Evolution Tracking

• Monitor new tool releases and capabilities
• Evaluate tool performance against current solutions
• Plan migration strategies for improved tools
• Maintain vendor relationship and feedback channels